An Automated Methodology for Identification and Analysis of Erroneous Production Stop Data

• Main Author: Soman, Sopal
• Format: Others
• Language: English
• Published: Högskolan i Skövde, Institutionen för ingenjörsvetenskap 2020
• Subjects: Robotics; Robotteknik och automation
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19126

The primary aim of the project is to automate the process of identifying erroneous entries in stop data originating from a given production line. Machines or work stations in a production line may be stopped due to various planned (scheduled maintenance, tool change, etc.) or unplanned (break downs, bottlenecks, etc.) reasons. It is essential to keep track of such stops for diagnosing inefficiencies such as reduced throughput and high cycle time variance. With the increased focus on Industry 4.0, many manufacturing companies have started to digitalize their production processes. Among other benefits, this has enabled production data to be captured in real-time and recorded for further analysis. However, such automation comes with its problems. In the case of production stop data, it has been observed that in addition to planned and unplanned stops, the data collection system may sometimes record erroneous or false stops. There are various known reasons for such erroneous stop data. These include not accounting for the lunch break, national holidays, weekends, communication loss with data collection system, etc. Erroneous stops can also occur due to unknown reasons, in which case they can only be identified through a statistical analysis of stop data distributions across various machines and workstations. This project presents an automated methodology that uses a combination of data filtering, aggregation, and clustering for identifying erroneous stop data with known reasons referred to as known faults. Once the clusters of known faults are identified, they are analyzed using association rule mining to reveal machines or workstations that are simultaneously affected. The ultimate goal of automatically identifying erroneous stop data entries is to obtain better empirical distribution for stop data to be used with simulation models. This aspect, along with the identification of unknown faults is open for future work.